Data-driven retailers are showing increased interest in process-related data from which performance metrics can be extracted. One such performance metric is a customer's time waiting in queues (particularly when the customer must perform actions at various stations along the queue), from which guidelines to improve order efficiency and customer satisfaction can be extracted. Millions of such queue-waiting customers are served via drive-thru operations every day. Slight improvements in efficiency or customer experience can have a large financial impact on such businesses. These retailers are heavily data driven, and they therefore have a strong interest in numerous customer and store metrics such as queue lengths, experience time in-store and drive-thru, specific order timing, order accuracy, and customer response.
Camera networks that employ a plurality of cameras and associating views are used to monitor vehicle movement in drive-thru businesses. However, such networks have usually been primarily implemented for security purposes, not customer service data acquisition. An important part of any system purposed for such data acquisition will require practical implementations of systems and methods for object tracking and timing across multiple camera views.
In situations where the size of areas of interest being video surveilled is significantly larger than the field of view of an individual camera, the use of camera networks consisting of multiple cameras which are typically intercommunicated through a central controller is desired. While such situations arise often, video monitoring of retailer drive-thru traffic is of particular interest to data-driven ones of such retailers who are especially interested in extracting performance metrics from the process-related data. One such performance metric is a customer's time waiting in various queues at the retail store, from which guidelines to improve order efficiency and customer satisfaction can be extracted. It is estimated that drive-thru operations account for 50 to 65% of a typical fast-food business. Consequently, the monetary benefits that would arise from more efficient drive-thru operations are significant. There is a need for improved video-based methods for object tracking and timing across multiple camera views. These methods are of interest since they can be applied in situations where drive-thru operation monitoring and statistics gathering is desired.
Within the context of video-based object tracking, object re-identification deals with the tracking of an object across fields of view of multiple cameras in camera networks, or across multiple fields of view of a single camera, such as those of a Pan-Tilt-Zoom (PTZ) camera. While significant work exists on object, vehicle and person re-identification, there is a need for improvements on the estimation of the timing of objects as they traverse the multiple fields of view of a set of cameras in a camera network. Individual timing information of an object is of interest in a wide variety of applications beyond retail applications, including surveillance (where alerts can be triggered if a specific person loiters in a specific area beyond a previously specified time length), fleet vehicle monitoring (e.g. monitoring and timing of delivery trucks, and public transportation vehicles such as taxis, buses and subways) and automated race-related statistic estimation (e.g. to automatically estimate lap times of vehicles, bicyclists, runners, etc.). A related technology to note is “point-to-point” speed measurement on a highway. Indeed, in that application times are determined that are associated with multiple camera views, but the technical problem and solution differs significantly from the present. In the highway scenario, the vehicles are moving at near constant speed at the time of a view and only one time per view needs to be captured to accurately determine speed between views that are possibly miles apart. In the drive-thru setting, specific times that occur within a given camera's view time are important. Further, the vehicles will stop, start, and change speed during a viewing time. One simple time recording per view as performed on highways will not represent timings relevant to drive-thru events.
Regarding the use of video analytics techniques in retail scenarios, significant efforts have been devoted to developing automated analysis of human queues in retail spaces. With regards to vehicle traffic management in a drive-thru scenario, systems exist for automatically detecting the presence of a vehicle via the use of RFID tags, for verifying the accuracy of a drive-thru product delivery, and to match transaction and visual customer data; however, no methods are known to automatically compute drive-thru queue statistics. Such statistics are particularly important to gauge the success of drive-thru installations which can be measured in terms of return of investment. In particular, there is a direct correlation between speed of service and customer satisfaction. In stores where consumer's perception of speed of service increases, same-store sales tend to increase as well. When a retailer has the desire to capture vehicle flow statistics, external consulting companies that are often employed to manually perform these tasks. In certain occasions, the restaurant employees themselves are equipped with timers or directly have control of equipment that measures elapsed customer queue waiting times by starting and stopping timers at the push of a button when an order is placed and delivered, respectively. These methods, however, typically lead to over optimistic timing estimates as they can easily be manipulated by the employees. There is thus a need for a system and method for object tracking and timing across multiple camera views that can be applied in tasks of drive-thru queue timing measurement and statistic estimation which would overcome the limitations of currently used methods.
FIG. 1 shows a plane view of a local quick service restaurant (“fast food”) building. Three locations of interest to the drive-thru process (order, pay and present points) are shown. The location where a drive-thru vehicle queue would form is also indicated. Clearly, unless aerial monitoring is an option (and even that solution will be susceptible to significant occlusion from the overhang roofs), a camera network setup is required to monitor drive-thru queue activity including points of interest.